I am performing a survival analysis on a dataset with many
variables. Multivariate cox proportional-hazard models
defined the best predictors (around 7 out of 270 variables). I
would like to give the readers some cut-off values
they can use in the clinical practice,

My fear is that with a selection of 7 variables out of 270, your
model shrinkage is likely to be a real problem. The associations you
observe in your data may not be reproducible. Normally, model
shrinkage is a function of the sample size and the number of
predictors, but there's a Harrel paper in Statistical Methods in
Medical Research (I'm sorry, I can't find it right now on the
shelves) that argues that in variable selection models, the shrinkage
is a function of the number of candidate variables, not the number in
the eventual model.